Re-ranking search results using language models of query-specific clusters

Abstract To obtain high precision at top ranks by a search performed in response to a query, researchers have proposed a cluster-based re-ranking paradigm: clustering an initial list of documents that are the most highly ranked by some initial search, and using information induced from these (often called) query-specific clusters for re-ranking the list. However, results concerning the effectiveness of various automatic cluster-based re-ranking methods have been inconclusive. We show that using query-specific clusters for automatic re-ranking of top-retrieved documents is effective with several methods in which clusters play different roles, among which is the smoothing of document language models. We do so by adapting previously-proposed cluster-based retrieval approaches, which are based on (static) query-independent clusters for ranking all documents in a corpus, to the re-ranking setting wherein clusters are query-specific. The best performing method that we develop outperforms both the initial document-based ranking and some previously proposed clusterbased re-ranking approaches; furthermore, this algorithm consistently outperforms a stateof-the-art pseudo-feedback-based approach. In further exploration we study the performance of cluster-based smoothing methods for re-ranking with various (soft and hard) clustering algorithms, and demonstrate the importance of clusters in providing context from the initial list through a comparison to using single documents to this end. Keywords Query-specific clusters Á Cluster-based language models Á Cluster-based re-ranking Á Cluster-based smoothing 1 Introductio

Building query-based relevance sets without human intervention

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophycollections are the standard framework used in the evaluation of an information retrieval system and the comparison between different systems. A text test collection consists of a set of documents, a set of topics, and a set of relevance assessments which is a list indicating the relevance of each document to each topic. Traditionally, forming the relevance assessments is done manually by human judges. But in large scale environments, such as the web, examining each document retrieved to determine its relevance is not possible. In the past there have been several studies that aimed to reduce the human effort required in building these assessments which are referred to as qrels (query-based relevance sets). Some research has also been done to completely automate the process of generating the qrels. In this thesis, we present different methodologies that lead to producing the qrels automatically without any human intervention. A first method is based on keyphrase (KP) extraction from documents presumed relevant; a second method uses Machine Learning classifiers, Naïve Bayes and Support Vector Machines. The experiments were conducted on the TREC-6, TREC-7 and TREC-8 test collections. The use of machine learning classifiers produced qrels resulting in information retrieval system rankings which were better correlated with those produced by TREC human assessments than any of the automatic techniques proposed in the literature. In order to produce a test collection which could discriminate between the best performing systems, an enhancement to the machine learning technique was made that used a small number of real or actual qrels as training sets for the classifiers. These actual relevant documents were selected by Losada et al.’s (2016) pooling technique. This modification led to an improvement in the overall system rankings and enabled discrimination between the best systems with only a little human effort. We also used the bpref-10 and infAP measures for evaluating the systems and comparing between the rankings, since they are more robust in incomplete judgment environments. We applied our new techniques to the French and Finnish test collections from CLEF2003 in order to confirm their reproducibility on non-English languages, and we achieved high correlations as seen for English